Literature DB >> 20708020

Investigation of the performance of fermentation processes using a mathematical model including effects of metabolic bottleneck and toxic product on cells.

Kansuporn Sriyudthsak1, Fumihide Shiraishi.   

Abstract

A number of recent research studies have focused on theoretical and experimental investigation of a bottleneck in a metabolic reaction network. However, there is no study on how the bottleneck affects the performance of a fermentation process when a product is highly toxic and remarkably influences the growth and death of cells. The present work therefore studies the effect of bottleneck on product concentrations under different product toxicity conditions. A generalized bottleneck model in a fed-batch fermentation is constructed including both the bottleneck and the product influences on cell growth and death. The simulation result reveals that when the toxic product strongly influences the cell growth and death, the final product concentration is hardly changed even if the bottleneck is removed, whereas it is markedly changed by the degree of product toxicity. The performance of an ethanol fermentation process is also discussed as a case example to validate this result. In conclusion, when the product is highly toxic, one cannot expect a significant increase in the final product concentration even if removing the bottleneck; rather, it may be more effective to somehow protect the cells so that they can continuously produce the product.
Copyright © 2010 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20708020     DOI: 10.1016/j.mbs.2010.08.001

Source DB:  PubMed          Journal:  Math Biosci        ISSN: 0025-5564            Impact factor:   2.144


  2 in total

1.  PASMet: a web-based platform for prediction, modelling and analyses of metabolic systems.

Authors:  Kansuporn Sriyudthsak; Ramon Francisco Mejia; Masanori Arita; Masami Yokota Hirai
Journal:  Nucleic Acids Res       Date:  2016-05-12       Impact factor: 16.971

2.  Identification of a metabolic reaction network from time-series data of metabolite concentrations.

Authors:  Kansuporn Sriyudthsak; Fumihide Shiraishi; Masami Yokota Hirai
Journal:  PLoS One       Date:  2013-01-10       Impact factor: 3.240

  2 in total

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